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In this paper we present an image predictive controller for an eye-in-hand-type servoing architecture, composed of a 6-d.o.f. robot and a camera mounted on the gripper. A novel architecture for integrating reference trajectory and image prediction is proposed for use in predictive control of visual servoing systems. In the proposed method, a new predictor is developed based on the relation between the camera velocity and the time variation of the visual features given by the interaction matrix. The image-based predictor generates the future trajectories of a visual feature ensemble when past and future camera velocities are known. In addition, a reference trajectory is introduced to define the way how to reach the desired features over the prediction horizon starting from the current features. The advantages of the new architecture are the reference trajectory used for the first time in the sense of the predictive control and the predictor based on a local model. Simulations reveal the efficiency of the proposed architecture to control a 6-d.o.f. robot manipulator. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, a control strategy based on fractional calculus for visual servoing systems is proposed. The image-based control strategy is designed using a point features based fractional-order PI controller. A real-time visual servoing system, composed of a manipulator robot with 6 degrees of freedom (d.o.f.) with an eye-in-hand camera, is used for performance evaluation of the proposed control strategy. The image acquisition and processing, together with the computing of the image-based control law are implemented in MATLAB. Using planar static objects, real-time experiments are conducted and the results reveal that the image-based fractional-order PI controller outperforms the conventional image-based integer-order PI controller. [Copyright &y& Elsevier]